For deep learning-based clinical applications, fully supervised learning is one of the most basic and popular solutions, especially in automatic multi-organ delineation systems. In this work, we investigate several existing SOTA methods on the WORD, including nnUNet [1]} and its variations (both 2D and 3D), ResUNet [2]}, DeepLabV3+ [3]}, UNet++ [4]} and Attention-UNet (AttUNet) [5]}. The quantitative segmentation results in term of \(DSC\)  and \(HD_{95}\)  are presented in Tab. REF  and Tab. REF . It can be found that all SOTA methods can achieve very promising results on large organs, such as the liver, spleen, kidney, stomach, bladder, and head of the femur. It has also proven that the large organs segmentation task is a well-solved problem if there are enough high-quality annotated samples. But for small organs, it remains very challenging and also desires to pay more attention, especially for some extremely small and unclear boundary organs adrenal and duodenum. However, there are very few works and researchers that focus on these challenging tasks. One of the key reasons lies in lacking large-scale and public available datasets. To solve this dilemma, we build the baseline and dataset to boost further researches in the medical computing community.
